7 papers
NOMANet: A Graph Neural Network Enabled Power Allocation Scheme for NOMA
Yipu Hou, Yang Lu, Wei Chen +3
This paper proposes a graph neural network (GNN) enabled power allocation scheme for non-orthogonal multiple access (NOMA) networks. In particular, a downlink scenario with one bas…
Graph Neural Network Enabled Pinching Antennas
Xinke Xie, Yang Lu, Zhiguo Ding
The pinching-antenna system is a novel flexible-antenna technology, which has the capabilities not only to combat large-scale path loss, but also to reconfigure the antenna array i…
ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels
Changpeng He, Yang Lu, Bo Ai +3
This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a q…
SWIPTNet: A Unified Deep Learning Framework for SWIPT based on GNN and Transfer Learning
Hong Han, Yang Lu, Zihan Song +5
This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sum-rate maxim…
Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach
Changpeng He, Yang Lu, Wei Chen +3
An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the tr…
Model-Based GNN Enabled Energy-Efficient Beamforming for Ultra-Dense Wireless Networks
Rongsheng Zhang, Yang Lu, Wei Chen +2
This paper investigates deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), named model-based…